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Histogram Specification

Histogram Specification. desired. -1. G. G. equalize. equalize. Histogram Specification (cont.). Equalize the levels of the original image. Specify the desired density function (histogram) and obtain the transformation function G(z).

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Histogram Specification

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  1. Histogram Specification desired -1 G G equalize equalize

  2. Histogram Specification (cont.) • Equalize the levels of the original image. • Specify the desired density function (histogram) and obtain the transformation function G(z). • Apply the inverse transformation function z = G (s) to the levels obtained in first step. -1

  3. Histogram Specification (cont.)-from text- • Saw what we want the histogram to look like and come up with a transform function that will give it to us. • Continuous random variables r & z. Pr(r) and Pz(z) denote their probability density functions. (Continuous equivalent of a histogram). • Pr(r) input image • Pz(z) desired output image (processed) This is what we'd like the processed image to have.

  4. Histogram Specification (cont.)-from text- • Continuous version of histogram equalization. • Cumulative Distribution Function

  5. Histogram Specification (cont.)-from text- • Continuous version of histogram equalization. • Cumulative distribution function.

  6. Histogram Specification (cont.)-from text- • If you have Pr(r) estimated from input image, you can obtain T(r) from Transformation G(z) can be obtained by using since you specified Pz(z)!

  7. Histogram Specification (cont.)-from text- -1 • Assume G exists and that is satisfys: • Single-Valued, monotonically increasing • 0 <= G (z) <= 1 for 0 <= z <= 1 -1

  8. Histogram Specification (cont.)-from text- • You can obtain an image with the specified probability density function from an input function using the following: • obtain T(r) using equilization • obtain G(z) by equalizing specified histogram • obtain G (z) • obtain the output image by applying G to all pixels in input image. -1 -1

  9. Histogram Specification (cont.)-from text- • So far so good (continuously speaking) • In practice it is difficult to obtain analytical expressions for T(r) and G • With discrete values it becomes possible to make a close approximation to the histogram. -1

  10. discrete Formulas L is the number of discrete gray levels n = total # pixels n = # of pixels with gray level j j

  11. Implementation • Each set of gray levels is a 1D array • All mappings from r to s and s to z are simple table lookups • Each element (e.g. s ) contains 2 important pieces of information: • subscript k denotes the location of the element in the array • s denotes the value at that location • We need to only look at integer values k

  12. Refer to Figure 3.19 GW • (a) a hypothetical transform function given an image that was equalized. • (b) Equalize specified histogram. G is just running the transform backwards. • But wait a minute! Where did we get the z's??? • We have to use an iterative scheme. -1

  13. Iterative Scheme • Obtain histogram of the given image. • Equalize the image to precompute a mapped level s for each r . T • Obtain G from the specified histogram by equalization. • Precompute z for each s using iterative scheme (3.3-17) • Map r -> s and back to z . • Moon example 3.4, page 100, 101, 102 GW k k k k k k k

  14. desired 0.3 0.3 0.2 0.2 0.1 0.1 0 1 4 6 7 0 1 4 6 7 2 3 5 2 3 5 0 – 0.0 1 – 0.0 2 – 0.0 3 – 0.15 4 – 0.20 5 – 0.30 6 – 0.20 7 – 0.15 0 – 0.19 1 – 0.25 2 – 0.21 3 – 0.16 4 – 0.08 5 – 0.06 6 – 0.03 7 – 0.02 1.0 1.0 0.8 0.8 0.6 0.6 0.4 0.4 0.2 0.2 0.0 0.0 0 1 4 6 7 2 3 5 0 1 4 6 7 2 3 5

  15. equalized histogram 0.3 0.3 0.2 0.2 0.1 0.1 r s 1 0 0 1 4 6 7 2 3 5 7 1.0 6 0.8 5 0.6 4 3 0.4 2 0.2 1 0.0 0 0 1 4 6 7 2 3 5

  16. equalized histogram 0.3 0.3 0.3 0.2 0.2 0.2 0.1 0.1 0.1 r s 1 0 0 1 4 6 7 2 3 5 1 3 7 1.0 6 0.8 5 0.6 4 3 0.4 2 0.2 1 0.0 0 0 1 4 6 7 2 3 5

  17. equalized histogram 0.3 0.3 0.3 0.2 0.2 0.2 0.1 0.1 0.1 r s 1 0 0 1 4 6 7 2 3 5 1 3 5 2 7 1.0 6 0.8 5 0.6 4 3 0.4 2 0.2 1 0.0 0 0 1 4 6 7 2 3 5

  18. equalized histogram 0.3 0.3 0.3 0.2 0.2 0.2 0.1 0.1 0.1 r s 1 0 0 1 4 6 7 2 3 5 1 3 5 2 7 1.0 6 3 6 0.8 5 0.6 4 3 0.4 2 0.2 1 0.0 0 0 1 4 6 7 2 3 5

  19. equalized histogram 0.3 0.3 0.3 0.2 0.2 0.2 0.1 0.1 0.1 r s 1 0 0 1 4 6 7 2 3 5 1 3 5 2 7 1.0 6 3 6 0.8 5 0.6 4 6 4 3 0.4 2 0.2 1 0.0 0 0 1 4 6 7 2 3 5

  20. equalized histogram 0.3 0.3 0.3 0.2 0.2 0.2 0.1 0.1 0.1 r s 1 0 0 1 4 6 7 2 3 5 1 3 5 2 7 1.0 6 3 6 0.8 5 0.6 4 6 4 3 0.4 5 7 2 0.2 1 0.0 0 0 1 4 6 7 2 3 5

  21. equalized histogram 0.3 0.3 0.3 0.2 0.2 0.2 0.1 0.1 0.1 r s 1 0 0 1 4 6 7 2 3 5 1 3 5 2 7 1.0 6 3 6 0.8 5 0.6 4 6 4 3 0.4 5 7 2 0.2 1 7 6 0.0 0 0 1 4 6 7 2 3 5

  22. equalized histogram 0.3 0.3 0.3 0.2 0.2 0.2 0.1 0.1 0.1 r s 1 0 0 1 4 6 7 2 3 5 0 1 4 6 7 2 3 5 1 3 5 2 7 1.0 6 3 6 0.8 5 0.6 4 6 4 3 0.4 5 7 2 0.2 1 7 6 0.0 0 7 7 0 1 4 6 7 2 3 5

  23. equalized histogram 0.3 0.3 0.3 0.2 0.2 0.2 0.1 0.1 0.1 r s 1 0 0 1 4 6 7 2 3 5 0 1 4 6 7 2 3 5 1 3 1 = 0.19 3 = 0.25 5 = 0.21 6 = .16+.08=.24 7 = .06+.03+.02 = 0.11 5 2 7 1.0 6 3 6 0.8 5 0.6 4 6 4 3 0.4 5 7 2 0.2 1 7 6 0.0 0 7 7 0 1 4 6 7 2 3 5

  24. desired equalized histogram 0.3 0.3 0.2 0.2 0.1 0.1 r s 0 0 0 1 4 6 7 0 2 3 5 1 0 1 4 6 7 2 3 5 2 0 7 1.0 3 1 6 0.8 2 4 5 0.6 4 4 5 3 0.4 2 0.2 6 6 1 0.0 7 7 0 0 1 4 6 7 2 3 5

  25. equalized orig resultant histogram 1 0 3 0.3 1 0.3 3 4 5 2 0.2 5 0.2 3 6 6 0.1 0.1 6 4 6 5 7 7 0 1 4 6 7 2 3 5 7 7 6 0 1 4 6 7 2 3 5 7 7 7 7 1.0 0.3 6 0.8 0.2 5 0.6 4 3 0.4 0.1 2 0.2 1 0.0 0 desired 0 1 4 6 7 2 3 5 0 1 4 6 7 2 3 5

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